
# using dfs for finding eulerian path traversal




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Union[str, Any]		,    _lowerCamelCase					:     Dict		,    _lowerCamelCase					:     Optional[int]		,    _lowerCamelCase					:     List[Any]=None)   ->       Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase      :  Dict       =   (path or []) + [u]
							for v in graph[u]:
														if visited_edge[u][v] is False:
																					__UpperCamelCase  ,		__UpperCamelCase      :  int       =   True, True
																					__UpperCamelCase      :  List[Any]       =   dfs(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)
							return path




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Union[str, Any]		,    _lowerCamelCase					:     List[str])   ->       Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase      :  Tuple       =   0
							__UpperCamelCase      :  Optional[Any]       =   -1
							for i in range(_lowerCamelCase):
														if i not in graph.keys():
																					continue
														if len(graph[i]) % 2 == 1:
																					odd_degree_nodes += 1
																					__UpperCamelCase      :  List[Any]       =   i
							if odd_degree_nodes == 0:
														return 1, odd_node
							if odd_degree_nodes == 2:
														return 2, odd_node
							return 3, odd_node




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[Any]		,    _lowerCamelCase					:     int)   ->       Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   [[False for _ in range(max_node + 1)] for _ in range(max_node + 1)]
							__UpperCamelCase  ,		__UpperCamelCase      :  Optional[Any]       =   check_circuit_or_path(_lowerCamelCase		,    _lowerCamelCase)
							if check == 3:
														print("graph is not Eulerian")
														print("no path")
														return
							__UpperCamelCase      :  Optional[int]       =   1
							if check == 2:
														__UpperCamelCase      :  Dict       =   odd_node
														print("graph has a Euler path")
							if check == 1:
														print("graph has a Euler cycle")
							__UpperCamelCase      :  Optional[int]       =   dfs(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)
							print(_lowerCamelCase)




def       _SCREAMING_SNAKE_CASE   (						)   ->       Optional[Any]:






							'''simple docstring'''





							__UpperCamelCase      :  Optional[int]       =   {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
							__UpperCamelCase      :  int       =   {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
							__UpperCamelCase      :  List[Any]       =   {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
							__UpperCamelCase      :  str       =   {1: [2, 3], 2: [1, 3], 3: [1, 2]}
							__UpperCamelCase      :  Tuple       =   {
							    1: [],
							    2: []
							    # all degree is zero
							}
							__UpperCamelCase      :  Optional[int]       =   10
							check_euler(_lowerCamelCase		,    _lowerCamelCase)
							check_euler(_lowerCamelCase		,    _lowerCamelCase)
							check_euler(_lowerCamelCase		,    _lowerCamelCase)
							check_euler(_lowerCamelCase		,    _lowerCamelCase)
							check_euler(_lowerCamelCase		,    _lowerCamelCase)


if __name__ == "__main__":
		main()

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase    :   Optional[int]						=			{
    'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig', 'MobileViTOnnxConfig'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[str]						=			['MobileViTFeatureExtractor']
		lowercase    :   Tuple						=			['MobileViTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[Any]						=			[
		    'MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'MobileViTForImageClassification',
		    'MobileViTForSemanticSegmentation',
		    'MobileViTModel',
		    'MobileViTPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[Any]						=			[
		    'TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFMobileViTForImageClassification',
		    'TFMobileViTForSemanticSegmentation',
		    'TFMobileViTModel',
		    'TFMobileViTPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_mobilevit import MobileViTFeatureExtractor
				from .image_processing_mobilevit import MobileViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilevit import (
				    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileViTForImageClassification,
				    MobileViTForSemanticSegmentation,
				    MobileViTModel,
				    MobileViTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mobilevit import (
				    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFMobileViTForImageClassification,
				    TFMobileViTForSemanticSegmentation,
				    TFMobileViTModel,
				    TFMobileViTPreTrainedModel,
				)


else:
		import sys

		lowercase    :   Optional[int]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)